Multipopulation-based multi-level parallel enhanced Jaya algorithms
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http://hdl.handle.net/10045/91222
Título: | Multipopulation-based multi-level parallel enhanced Jaya algorithms |
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Autor/es: | Migallón Gomis, Héctor | Jimeno-Morenilla, Antonio | Sanchez-Romero, Jose-Luis | Rico, Héctor | Rao, Ravipudi Venkata |
Grupo/s de investigación o GITE: | UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Jaya | Optimization | Metaheuristic | Multipopulation | Parallelism | MPI/OpenMP |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | mar-2019 |
Editor: | Springer US |
Cita bibliográfica: | The Journal of Supercomputing. 2019, 75(3): 1697-1716. doi:10.1007/s11227-019-02759-z |
Resumen: | To solve optimization problems, in the field of engineering optimization, an optimal value of a specific function must be found, in a limited time, within a constrained or unconstrained domain. Metaheuristic methods are useful for a wide range of scientific and engineering applications, which accelerate being able to achieve optimal or near-optimal solutions. The metaheuristic method called Jaya has generated growing interest because of its simplicity and efficiency. We present Jaya-based parallel algorithms to efficiently exploit cluster computing platforms (heterogeneous memory platforms). We propose a multi-level parallel algorithm, in which, to exploit distributed-memory architectures (or multiprocessors), the outermost layer of the Jaya algorithm is parallelized. Moreover, in internal layers, we exploit shared-memory architectures (or multicores) by adding two more levels of parallelization. This two-level internal parallel algorithm is based on both a multipopulation structure and an improved heuristic search path relative to the search path of the sequential algorithm. The multi-level parallel algorithm obtains average efficiency values of 84% using up to 120 and 135 processes, and slightly accelerates the convergence with respect to the sequential Jaya algorithm. |
Patrocinador/es: | This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R and Grant TIN2017-89266-R, co-financed by FEDER funds (MINECO/FEDER/UE). |
URI: | http://hdl.handle.net/10045/91222 |
ISSN: | 0920-8542 (Print) | 1573-0484 (Online) |
DOI: | 10.1007/s11227-019-02759-z |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s11227-019-02759-z |
Aparece en las colecciones: | INV - UNICAD - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2019_Migallon_etal_JSupercomputing_final.pdf | Versión final (acceso restringido) | 1,1 MB | Adobe PDF | Abrir Solicitar una copia |
2019_Migallon_etal_JSupercomputing_preprint.pdf | Preprint (acceso abierto) | 397,92 kB | Adobe PDF | Abrir Vista previa |
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